Delete ChinaOpen.py

ChinaOpen.py

@@ -1,124 +0,0 @@
-
-import json
-
-import datasets
-from datasets.tasks import QuestionAnsweringExtractive
-
-
-logger = datasets.logging.get_logger(__name__)
-
-_CITATION = """\
-@article{2016arXiv160605250R,
-       author = {{Rajpurkar}, Pranav and {Zhang}, Jian and {Lopyrev},
-                 Konstantin and {Liang}, Percy},
-        title = "{SQuAD: 100,000+ Questions for Machine Comprehension of Text}",
-      journal = {arXiv e-prints},
-         year = 2016,
-          eid = {arXiv:1606.05250},
-        pages = {arXiv:1606.05250},
-archivePrefix = {arXiv},
-       eprint = {1606.05250},
-}
-"""
-
-_DESCRIPTION = """\
-Stanford Question Answering Dataset (SQuAD) is a reading comprehension \
-dataset, consisting of questions posed by crowdworkers on a set of Wikipedia \
-articles, where the answer to every question is a segment of text, or span, \
-from the corresponding reading passage, or the question might be unanswerable.
-"""
-
-
-
-#_URL = "./ChinaOpen-1k.zip"
-_URLS = {
-    "train": "https://huggingface.co/datasets/AIMClab/ChinaOpen/tree/main/data"
-}
-
-
-
-class ChinaOpenConfig(datasets.BuilderConfig):
-    """BuilderConfig for SQUAD."""
-
-    def __init__(self, **kwargs):
-        """BuilderConfig for SQUAD.
-        Args:
-          **kwargs: keyword arguments forwarded to super.
-        """
-        super(ChinaOpenConfig, self).__init__(**kwargs)
-
-
-class ChinaOpen(datasets.GeneratorBasedBuilder):
-    """SQUAD: The Stanford Question Answering Dataset. Version 1.1."""
-
-    BUILDER_CONFIGS = [
-        ChinaOpenConfig(
-            name="plain_text",
-            version=datasets.Version("1.0.0", ""),
-            description="Plain text",
-        ),
-    ]
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "id": datasets.Value("string"),
-                    "title": datasets.Value("string"),
-                    "context": datasets.Value("string"),
-                    "question": datasets.Value("string"),
-                    "answers": datasets.features.Sequence(
-                        {
-                            "text": datasets.Value("string"),
-                            "answer_start": datasets.Value("int32"),
-                        }
-                    ),
-                }
-            ),
-            # No default supervised_keys (as we have to pass both question
-            # and context as input).
-            supervised_keys=None,
-            citation=_CITATION,
-            task_templates=[
-                QuestionAnsweringExtractive(
-                    question_column="question", context_column="context", answers_column="answers"
-                )
-            ],
-        )
-
-    def _split_generators(self, dl_manager):
-        downloaded_files = dl_manager.download_and_extract(_URLS)
-
-        return [
-            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
-            #datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}),
-        ]
-'''
-    def _generate_examples(self, filepath):
-        """This function returns the examples in the raw (text) form."""
-        logger.info("generating examples from = %s", filepath)
-        key = 0
-        with open(filepath, encoding="utf-8") as f:
-            squad = json.load(f)
-            for article in squad["data"]:
-                title = article.get("title", "")
-                for paragraph in article["paragraphs"]:
-                    context = paragraph["context"]  # do not strip leading blank spaces GH-2585
-                    for qa in paragraph["qas"]:
-                        answer_starts = [answer["answer_start"] for answer in qa["answers"]]
-                        answers = [answer["text"] for answer in qa["answers"]]
-                        # Features currently used are "context", "question", and "answers".
-                        # Others are extracted here for the ease of future expansions.
-                        yield key, {
-                            "title": title,
-                            "context": context,
-                            "question": qa["question"],
-                            "id": qa["id"],
-                            "answers": {
-                                "answer_start": answer_starts,
-                                "text": answers,
-                            },
-                        }
-                        key += 1
-'''